Missing Crop Vegetation Areas Detection with Python and Scikit Learn

An applied case for the recognition of missing crop vegetation areas based on a drone orthophoto. Contours have been identified from an enhanced combination of raster bands with a marching squares method to find constant valued contours and then exported as geospatial polygons. Webinar content: - A python class to find crop rows - Remove shadows on the raster image - Define missing vegetation by marching squares - Export results as geospatial polygon

Instructor / Instructor:

Saul Montoya M.Sc

Hydrogeologist - Numerical Modeler

Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.

Language / Lenguaje:

English

Event date / Fecha del evento:

Monday, Dec 04 2023 6:00 p.m. Amsterdam Time

Hosted by / Organizado por:

Hatarilabs

Stream link / Enlace de transmisión:

https://meet.google.com/fwn-totg-ywc

Input data / Datos de entrada:

https://owncloud.hatarilabs.com/s/2LjVQVGDIcdy0Xy

Additional instructions / Instrucciones adicionales:

Password to download data: Hatarilabs. You need to have Anaconda installed on your computer.